BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection

TL;DR

BotTrans introduces a multi-source graph domain adaptation method that improves social bot detection by addressing network heterophily and leveraging multiple source domains for more effective knowledge transfer.

Contribution

The paper proposes BotTrans, a novel multi-source graph domain adaptation model that enhances social bot detection by increasing network homophily and integrating relevance between source and target domains.

Findings

Outperforms state-of-the-art methods on real-world datasets

Effectively leverages multi-source knowledge for improved detection

Addresses network heterophily to enhance model learning

Abstract

Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfer faces two critical challenges. Firstly, the network heterophily problem, which is caused by bots hiding malicious behaviors via indiscriminately interacting with human users, hinders the model's ability to learn sufficient and accurate bot-related knowledge from source domains. Secondly, single-source transfer might lead to inferior and unstable results, as the source network may embody weak relevance to the task and provide limited knowledge. To address these challenges, we explore multiple source domains and propose a multi-source graph domain adaptation model named \textit{BotTrans}. We initially leverage the labeling knowledge shared across multiple source networks to establish a cross-source-domain topology with increased network homophily. We then aggregate cross-domain neighbor information to enhance the discriminability of source node embeddings. Subsequently, we integrate the relevance between each source-target pair with model optimization, which facilitates knowledge transfer from source networks that are more relevant to the detection task. Additionally, we propose a refinement strategy to improve detection performance by utilizing semantic knowledge within the target domain. Extensive experiments on real-world datasets demonstrate that \textit{BotTrans} outperforms the existing state-of-the-art methods, revealing its efficacy in leveraging multi-source knowledge when the target detection task is unlabeled.